1. Field of the Invention
The invention relates generally to a method and system for improved computerized segmentation and discrimination of lesions in medical images. Novel techniques in the segmentation of lesions include radial gradient segmentation and probabilistic segmentation using techniques such as constraint functions.
The present invention claims priority to U.S. patent application Ser. No. 08/900,361, filed Jul. 25, 1997, the contents of which are incorporated by reference herein. The present invention generally relates to CAD techniques for automated detection of abnormalities in digital images, for example, as disclosed in one or more of U.S. Pat. Nos. 4,839,807; 4,841,555; 4,851,984; 4,875,165; 4,907,156; 4,918,534; 5,072,384; 5,133,020; 5,150,292; 5,224,177; 5,289,374; 5,319,549; 5,343,390; 5,359,513; 5,452,367; 5,463,548; 5,491,627; 5,537,485; 5,598,481; 5,622,171; 5,638,458; 5,657,362; 5,666,434; 5,673,332; 5,668,888; and 5,740,268; as well as U.S. patent application Ser. Nos. 08/158,388; 08/173,935; 08/220,917; 08/398,307; 08/428,867; 08/523,210; 08/536,149; 08/536,450; 08/515,798; 08/562,087; 08/757,611; 08/758,438; 08/900,191; 08/900,361; 08/900,362; 08/900,188; and 08/900,189, 08/900,192; 08/979,623; 08/979,639; 08/982,282; 09/027,468; 09/027,685; 09/028,518; 09/053,798; 09/092,004; 09/098,504; and 09/121,719 all of which are incorporated herein by reference.
The present invention includes use of various technologies referenced and described in the above-noted U.S. Patents and Applications, as well as described in the references identified in the appended APPENDIX and cross-referenced throughout the specification by reference to the number, in brackets and bold print, of the respective reference listed in the APPENDIX, the entire contents of which, including the related patents and applications listed above and references listed in the APPENDIX, are incorporated herein by reference.
2. Discussion of the Background
The segmentation of lesions from surrounding background is a vital step in many computerized mass-detection schemes for digital (or digitized) mammograms. Once the lesion is segmented, features can then be calculated using the segmentation information and more accurate classification can be accomplished.
Numerous techniques have been developed to segment lesions from surrounding tissues in digital mammograms. Petrick et al. [1] employed density-weighted contrast enhancement (DWCE) segmentation to extract lesions and potential lesions from their surrounding tissues. Comer et al. [2] and Li et al. [3] used Markov random fields to classify the different regions in a mammogram based on texture. A lesion segmentation algorithm was developed by Sameti et al. [4] which uses fuzzy sets to partition the mammographic image data. Despite the difficulty and importance of this step in many computerized mass-detection schemes, few have attempted to analyze the performance of these segmentation algorithms alone, choosing instead to collectively analyze all components of a scheme.
Here two methods are presented for segmenting lesions in digital or digitized mammograms, the radial gradient index (RGI)-based algorithm and a probabilistic algorithm. These techniques are seeded segmentation algorithms, which means that they begin with a point, called the seed point, that is defined to be within the suspect lesion. Many current computerized mass-detection schemes first employ an initial detection algorithm which returns locations that are used as seed points for the segmentation algorithm. In our previous works [5], a region-growing algorithm [6,7] was performed to extract the lesion from its surrounding tissues. Region-growing is a local thresholding process which utilizes only the gray-level information around the seed point. A series of partitions containing the seed point is created by thresholding and a rule determines which partition is to represent the partition of the suspect lesion. Potential problems with this algorithm are that the rules devised to choose the suspect lesion's partition are heuristic and often based on the first or second derivatives of noisy data.